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Practicing Machine Learning Interview Questions in Python

Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.

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4 Hours16 Videos60 Exercises
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Course Description

Prepare for Your Machine Learning Interview

Have you ever wondered how to properly prepare for a Machine Learning Interview? In this course, you will prepare answers for 15 common Machine Learning (ML) in Python interview questions for a data scientist role.

These questions will revolve around seven important topics: data preprocessing, data visualization, supervised learning, unsupervised learning, model ensembling, model selection, and model evaluation.

Refresh Your Machine Learning Knowledge

You’ll start by working on data pre-processing and data visualization questions. After performing all the preprocessing steps, you’ll create a predictive ML model to hone your practical skills.

Next, you’ll cover some supervised learning techniques before moving on to unsupervised learning. Depending on the role, you’ll likely cover both topics in your machine learning interview.

Finally, you’ll finish by covering model selection and evaluation, looking at how to evaluate performance for model generalization, and look at various techniques as you build an ensemble model.

Practice Answers to the Most Common Machine Learning Interview Questions

By the end of the course, you will possess both the required theoretical background and the ability to develop Python code to successfully answer these 15 questions.

The coding examples will be mainly based on the scikit-learn package, given its ease of use and ability to cover the most important machine learning techniques in the Python language.

The course does not teach machine learning fundamentals, as these are covered in the course's prerequisites.
  1. 1

    Data Pre-processing and Visualization

    Free

    In the first chapter of this course, you'll perform all the preprocessing steps required to create a predictive machine learning model, including what to do with missing values, outliers, and how to normalize your dataset.

    Play Chapter Now
    Handling missing data
    50 xp
    The hunt for missing values
    100 xp
    Simple imputation
    100 xp
    Iterative imputation
    100 xp
    Data distributions and transformations
    50 xp
    Training vs test set distributions and transformations
    50 xp
    Train/test distributions
    100 xp
    Log and power transformations
    100 xp
    Data outliers and scaling
    50 xp
    Outlier detection
    100 xp
    Handling outliers
    100 xp
    Z-score standardization
    100 xp
  2. 2

    Supervised Learning

    In the second chapter of this course, you'll practice different several aspects of supervised machine learning techniques, such as selecting the optimal feature subset, regularization to avoid model overfitting, feature engineering, and ensemble models to address the so-called bias-variance trade-off.

    Play Chapter Now
    Regression: feature selection
    50 xp
    Best feature subset
    50 xp
    Filter and wrapper methods
    100 xp
    Feature selection through feature importance
    100 xp
    Regression: regularization
    50 xp
    Avoiding overfitting
    50 xp
    Lasso regularization
    100 xp
    Ridge regularization
    100 xp
    Classification: feature engineering
    50 xp
    Classification model features
    50 xp
    Logistic regression baseline classifier
    100 xp
    Ensemble methods
    50 xp
    Bootstrap aggregation (bagging)
    100 xp
    Boosting
    100 xp
    XG Boost
    100 xp
  3. 3

    Unsupervised Learning

    In the third chapter of this course, you'll use unsupervised learning to apply feature extraction and visualization techniques for dimensionality reduction and clustering methods to select not only an appropriate clustering algorithm but optimal cluster number for a dataset.

    Play Chapter Now
    Dimensionality reduction: feature extraction
    50 xp
    The curse of dimensionality
    50 xp
    Principal component analysis
    100 xp
    Singular value decomposition
    100 xp
    Dimensionality reduction: visualization techniques
    50 xp
    Reducing high-dimensional data
    50 xp
    Visualization separation of classes with PCA I
    100 xp
    Visualization PCs with a scree plot
    100 xp
    Clustering analysis: selecting the right clustering algorithm
    50 xp
    Clustering algorithms
    50 xp
    K-means clustering
    100 xp
    Hierarchical agglomerative clustering
    100 xp
    Clustering analysis: choosing the optimal number of clusters
    50 xp
    What is the optimal k?
    50 xp
    Silhouette method
    100 xp
    Elbow method
    100 xp
  4. 4

    Model Selection and Evaluation

    In the fourth and final chapter of this course, you'll really step it up and apply bootstrapping and cross-validation to evaluate performance for model generalization, resampling techniques to imbalanced classes, detect and remove multicollinearity, and build an ensemble model.

    Play Chapter Now
    Model generalization: bootstrapping and cross-validation
    50 xp
    Validating model performance
    50 xp
    Decision tree
    100 xp
    A forest of decision trees
    100 xp
    Model evaluation: imbalanced classification models
    50 xp
    X-ray weapon detection
    50 xp
    Imbalanced class metrics
    100 xp
    Resampling techniques
    100 xp
    Model selection: regression models
    50 xp
    Addressing multicollinearity
    50 xp
    Multicollinearity techniques - feature engineering
    100 xp
    Multicollinearity techniques - PCA
    100 xp
    Model selection: ensemble models
    50 xp
    Random forest vs gradient boosting
    50 xp
    Random forest ensemble
    100 xp
    Gradient boosting ensemble
    100 xp
    Wrap-Up
    50 xp

Datasets

DiabetesLoans datasetLoans training set (reduced)

Collaborators

Collaborator's avatar
Adel Nehme

Prerequisites

Unsupervised Learning in PythonSupervised Learning with scikit-learn
Lisa Stuart HeadshotLisa Stuart

Data Scientist

Lisa Stuart is a Data Scientist with a wealth of industry experience. She is currently on the LSLPG Data Science Team at Thermo Fisher Scientific where she and her team build solutions to support the company motto to 'make the world healthier, cleaner and safer.' Prior to that, she built predictive models for targeted marketing at Costco and Expedia and managed dashboards for process automation. At Starbucks, she managed a team of data scientists to build a predictive model on geopolitical stability of countries around the world to make informed decisions on expansion and supply routes. As part of the DSP Big Data Analytics Team at Amazon, she and her team used statistical analysis and machine learning to improve processes around successful and on-time delivery for each and every Amazon order. In her free time, you'll find her at the dog park hanging out with her beloved dogs Blaze, Stella and Kona.
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